Joint optimization of battery mass and flight trajectory for high-altitude solar-powered aircraft

XZ Gao, ZX Hou, Z Guo, XQ Chen… - Proceedings of the …, 2014 - journals.sagepub.com
XZ Gao, ZX Hou, Z Guo, XQ Chen, XQ Chen
Proceedings of the Institution of Mechanical Engineers, Part G …, 2014journals.sagepub.com
The design parameters of high-altitude solar-powered aircraft are highly correlative with its
flight trajectory. However, it is not an easy work to jointly optimize them in the concept design
stage. This paper considers the joint optimization problem of battery mass and flight
trajectory for high-altitude solar-powered aircraft. The system model including the aircraft
dynamic model, aerodynamic parameters, and thrust model is presented. Then the problem
to be optimized is formulated and a new optimization method, which uses the particle swarm …
The design parameters of high-altitude solar-powered aircraft are highly correlative with its flight trajectory. However, it is not an easy work to jointly optimize them in the concept design stage. This paper considers the joint optimization problem of battery mass and flight trajectory for high-altitude solar-powered aircraft. The system model including the aircraft dynamic model, aerodynamic parameters, and thrust model is presented. Then the problem to be optimized is formulated and a new optimization method, which uses the particle swarm optimization and Gauss pseudo-spectral method, is proposed. The Gauss pseudo-spectral method is employed to generate the minimal power consumed by following the flight trajectory in the given configuration of high-altitude solar-powered aircraft, while the particle swarm optimization is used to calculate the optimal battery mass of aircraft. The simulation result shows that the proposed joint optimization method can reduce the battery mass of high-altitude solar-powered aircraft from 16 kg to 13.6 kg, which is equivalent to enhancing its energy density by 19.7%. It can be also seen that the proposed optimization method connects each parameter in a logically clear way and hence provide a perspective for understanding the optimization problem.
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